Expert systems for making credit recommendations are generally based on numeric scoring and do not particularly reflect the decision process of a credit expert. This invention, on the other hand, comprises an expert credit method and system which use a decision matrix tree to emulate the decision process of credit experts in analyzing a credit applicant and recommending whether to extend the credit sought.
The method used is to construct and implement, for example, in a software-controlled general purpose digital computer, a decision matrix tree. The tree is constructed so as to emulate the thought processes of human credit experts. Each matrix in the tree compares two pertinent characteristics and in turn represents, and provides as output, another pertinent characteristic which is then available as input to a subsequent matrix in the tree. In each matrix, each of the two characteristics being compared is depicted on one axis of the matrix. Each of the two axes of the matrix has provision for a number of different values of the characteristic depicted on that axis. Each box in the matrix represents a different combination of values of those two input characteristics. For each of these possible combinations, the matrix determines the value of the characteristic represented by the matrix itself. Thus, each matrix accepts as input two values, one on each axis. Each matrix delivers as output a single value, determined by the value of the matrix box defined by the two values on the axes.
Each input value to a matrix emanates from one of two sources. One or both input values for a matrix may be output from preceding matrixes in the matrix decision tree. Other input values are supplied from the "knowledge base," which is a database resident in or accessible to the computer and which has been supplied with information derived from credit experts.
The form of the decision matrix tree, as well as the identity of the characteristics compared in the various matrixes, the output characteristic from each matrix, the order of the matrices, and the designations of values in each box of each matrix are all the product of extensive contribution by credit experts.
A preferred embodiment of the invention employs a general purpose computer under the control of rule-based expert system artificial intelligence software that emulates the decision process of experts in the field of floorplan inventory lending. Industry characteristics and lending practices unique to floorplan lending play a fundamental role in the underlying decision matrix tree and characteristics ("criteria") used in this method and system. As opposed to a numeric scoring method or system, this invention actually compares various factors used by experts in making decisions, to emulate more closely the experts' decision process in reaching a recommendation on whether to extend credit.
In addition to recommending a determination on whether to extend the credit sought, the method and system of the invention may provide output, usually in the form of computer print-out or computer screen displays, on matters supporting the ultimate recommendation. For example, the method and system may provide as output a summary of the criteria input, and the recommendation, in a prescribed format. Alternatively or in addition, the method and system may provide as output a sequence of observations regarding criteria perceived as positive, and a sequence of observations regarding criteria perceived as negative. The output may include observations made directly by the user (and input to the computer) as well as those generated by the invention. The output from the invention may also include detailed listings and analyses of various criteria deemed significant to the recommendation (e.g., information relating to financial statements). This aspect of the invention is of assistance in making manifest the rationale behind the recommendation. It is also useful in training personnel in the credit determination process.
The expert method and system is designed to aid and train floorplan credit analysts in making credit decisions while ensuring consistency in credit policy. The system is designed for use on personal computers. The preferred embodiment operates in connection with IBM.RTM. and IBM-compatible personal computers having at least a 640K byte memory, a 20 megabyte disk drive and a printer. Analysts input information into the system, which provides them with results of an extensive analysis both on screen and through several printed reports.
The method and system involve two interacting components. The first component is a database management program which also comprises a user interface (generally a screen display) that prompts an individual analyst for all information pertinent to a credit decision. In a preferred embodiment of the invention, PARADOX.RTM. relational data base software (version 3.01), marketed by Borland International, is used as the database manager. The information input by the user in response to prompts includes the type of credit line requested, the level of management experience, types of collateral and inventory, type of business or industry, credit history and financial information. Mathematical. calculations are performed on certain of this information by stored software which generates financial and other ratios pertinent to a decision. This data is then maintained in the database for retrieval at any time. The software may also provide for the electronic transfer of client data to other locations, e.g., regional offices.
The second component of the method and system is a computer program written in a language suited to artificial intelligence expert systems, such as Prolog Version 5.x, marketed by Arity Corporation. This program is the "brain" of the method and system, analyzing client information that it obtains from the database manager. The analysis performed by the program emulates the reasoning patterns of floorplan credit experts by using a decision matrix tree, comprising a plurality of decision matrices arranged in branches. To arrive at the final recommendation to grant or deny credit, or further review the matter, the program must perform each of the operations in each branch of the decision tree.
At the initial stage, the process and system compare each of a number of specific criteria to levels or ranges characterized in a particular manner by credit experts for the specific business or industry. Some of the relevant criteria are: direct payment experience of the user with the borrower, payment experience of other creditors, industry experience of the borrower's management, personal character of management, strength of industry, strength of product sold, strength of suppliers, type and strength of collateral, type and strength of documentation, sales level and trend, gross margins and trend, net margins and trend, leverage position and trend, and liquidity position and trend.
With the information obtained from this analysis, the process proceeds through several series of matrices arranged in branches whereby the strengths and weaknesses of each criterion category are compared with strengths and weaknesses of one or more other criterion categories. The result of each such comparison is then used in another comparison in a subsequent decision. Consideration is given to the significance of each category relative to the overall decision by its position in the tree. Those categories deemed more important in the final decision are dealt with last. This analysis process is unlike credit scoring systems because numeric weights are not used. Instead, the program actually considers the strength of one criterion in relation to another criterion, making many such decisions throughout the analysis process. For example, the program may compare components of a balance sheet and reach its decision based on the overall strength of the balance sheet alone. At the same level, the program is comparing other components to decide the strength of the balance sheet trend, income statement and the income statement trend. The analysis continues by then comparing these new decisions to one another. This method is duplicated for all aspects of the decision process.
As the analysis progresses, and based on the criteria and results of criteria comparisons, the program constructs comments unique to each client indicating the status of each important element of floorplan lending. Upon completion these comments are transferred to the database management system for storage and retrieval.
Output from the method and system may include an "Executive Summary" designed to provide a quick look at the condition of the most significant factors, positive and negative, which pertain to the credit decision. Comments presented in this summary are generated through the analysis process and are unique to each client. The output from the method and system may also include a standard report indicating the result of each major step in the analysis and a summary of data input. Additionally, the method and system may provide standardized financial spreadsheets which include financial ratios relating to inventory financing.
The output from this method and system is used as a tool in making credit decisions and can also be used for training credit analysts in the proper requirements of floorplan lending. Use of the method and system helps a lending organization ensure a uniform standard in the credit evaluation process and helps to eliminate arbitrary considerations from the decision. It also provides a means for better and more thorough supervision of the credit decision processes of subordinates. The system further highlights key issues, both positive and negative, and by inference indicates those areas that need to be addressed in order to make a transaction more favorable.